Method for computing and using timing errors that occur in multiples predicted by multiple prediction algorithm

ABSTRACT

Method for computing one or more timing errors that occur in one or more multiples predicted by a multiple prediction algorithm. The method includes the steps of generating one or more actual three-dimensional primary travel times and one or more actual three-dimensional multiple travel times, applying the multiple prediction algorithm to the actual three-dimensional primary travel times to generate one or more travel times for the multiples predicted by the multiple prediction algorithm, and subtracting the actual three-dimensional multiple travel times from the travel times for the multiples predicted by the multiple prediction algorithm.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. provisional patent applicationSer. No. 60/467,376, filed May 2, 2003, which is herein incorporated byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the present invention generally relate to marine seismicsurveying and, more particularly, to a method for eliminating surfacerelated multiples from a record of seismic data.

2. Description of the Related Art

Seismic surveying is a method for determining the structure ofsubterranean formations in the earth. Seismic surveying typicallyutilizes seismic energy sources, which generate seismic waves, andseismic receivers, which detect seismic waves. The seismic wavespropagate into the formations in the earth, where a portion of the wavesreflects from interfaces between subterranean formations. The amplitudeand polarity of the reflected waves are determined by the differences inacoustic impedance between the rock layers comprising the subterraneanformations. The acoustic impedance of a rock layer is the product of theacoustic propagation velocity within the layer and the density of thelayer. The seismic receivers detect the reflected seismic waves andconvert the reflected waves into representative electrical signals. Thesignals are typically transmitted by electrical, optical, radio or othermeans to devices that record the signals. Through analysis of therecorded signals (or traces), the shape, position and composition of thesubterranean formations can be determined.

Marine seismic surveying is a method for determining the structure ofsubterranean formations underlying bodies of water. Marine seismicsurveying typically utilizes seismic energy sources and seismicreceivers located in the water, which are either towed behind a vesselor positioned on the water bottom from a vessel. The energy source istypically an explosive device or compressed air system that generatesseismic energy, which then propagates as seismic waves through the bodyof water and into the earth formations below the bottom of the water. Asthe seismic waves strike interfaces between subterranean formations, aportion of the seismic waves reflects back through the earth and waterto the seismic receivers, to be detected, transmitted, and recorded. Theseismic receivers typically used in marine seismic surveying arepressure sensors, such as hydrophones. Additionally, motion sensors,such as geophones or accelerometers may be used. Both the sources andreceivers may be strategically repositioned to cover the survey area.

Seismic waves, however, reflect from interfaces other than just thosebetween subterranean formations, as would be desired. Seismic waves alsoreflect from the water bottom and the water surface, and the resultingreflected waves themselves continue to reflect. Waves that reflectmultiple times are referred to as multiple reflections or “multiples”.Surface multiples are those waves that have reflected multiple timesbetween the water surface and any upward reflectors, such as the waterbottom or formation interfaces, before being sensed by a receiver.Generally, surface multiples are considered undesirable noises thatinterfere with and complicate the desired signal. Considerable effort isexpended in the design of seismic data acquisition and the processing ofseismic data to limit the impact of multiple reflections on the finalprocessed seismic profiles. Even so, in many areas, the quality ofseismic data is lowered, sometimes substantially, by the presence ofmultiple reflections.

Various prior art methods have been tried for removal or elimination ofsurface multiples from recorded traces. If the subsurface reflectors areflat or have dips in one direction, then a one-dimensional dataacquisition geometry may provide sufficient information for the surfacemultiples to be predicted using a two-dimensional multiple predictionalgorithm. However, if the subsurface reflectors have dips in more thanone direction or the data acquisition geometry is not one-dimensional(e.g., due to ocean currents), then surface multiples may only beaccurately predicted by a three-dimensional multiple predictionalgorithm, which is generally more costly than a two-dimensionalmultiple prediction algorithm.

Accordingly, a need exists in the art for a method for determining whenit would be necessary to perform a two-dimensional multiple predictionalgorithm versus a three-dimensional multiple prediction algorithm toavoid unnecessary costly expenditure.

SUMMARY OF THE INVENTION

Embodiments of the present invention are generally directed to a methodfor computing one or more timing errors that occur in one or moremultiples predicted by a multiple prediction algorithm. The methodincludes the steps of generating one or more actual three-dimensionalprimary travel times and one or more actual three-dimensional multipletravel times, applying the multiple prediction algorithm to the actualthree-dimensional primary travel times to generate one or more traveltimes for the multiples predicted by the multiple prediction algorithm,and subtracting the actual three-dimensional multiple travel times fromthe travel times for the multiples predicted by the multiple predictionalgorithm.

In one embodiment, the actual three-dimensional travel times arecomputed by applying a ray tracing method to a three-dimensional earthmodel.

In another embodiment, the actual three-dimensional travel times arecomputed by applying a ray tracing method to a three-dimensional earthmodel and one or more data acquisition geometry parameters.

Embodiments of the present invention are also directed to a method foroptimizing a marine survey design. The method includes the steps ofgenerating a cross line aperture, comparing the cross line aperture witha previous cross line aperture, and modifying one or more dataacquisition geometry based on a result of the comparison between thecross line aperture with the previous cross line aperture.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 illustrates a schematic view of marine seismic surveying.

FIG. 2 illustrates a flow diagram of a method for computing timingerrors that occur in multiple reflections predicted by a multipleprediction algorithm in accordance with one embodiment of the invention.

FIG. 3 illustrates a flow diagram of a two-dimensional predictionalgorithm for generating travel times for multiple reflections predictedby the two-dimensional prediction algorithm in accordance with oneembodiment of the invention.

FIG. 4 illustrates a method for optimizing a marine survey design inaccordance with one embodiment of the invention.

FIG. 5 illustrates a computer network into which embodiments of theinvention may be implemented.

DETAILED DESCRIPTION

FIG. 1 illustrates a schematic view of marine seismic surveying 100.Subterranean formations to be explored, such as 102 and 104, lie below abody of water 106. Seismic energy sources 108 and seismic receivers 110are positioned in the body of water 106, typically by one or moreseismic vessels (not shown). A seismic source 108, such as an air gun,creates seismic waves in the body of water 106 and a portion of theseismic waves travels downward through the water toward the subterraneanformations 102 and 104 beneath the body of water 106. When the seismicwaves reach a seismic reflector, a portion of the seismic waves reflectsupward and a portion of the seismic waves continues downward. Theseismic reflector can be the water bottom 112 or one of the interfacesbetween subterranean formation, such as interface 114 between formations102 and 104. When the reflected waves traveling upward reach thewater/air interface at the water surface 116, a majority portion of thewaves reflects downward again. Continuing in this fashion, seismic wavescan reflect multiple times between upward reflectors, such as the waterbottom 112 or formation interface 114, and the downward reflector at thewater surface 116 above, as described more fully below. Each time thereflected waves propagate past the position of a seismic receiver 110,the receiver 110 senses the reflected waves and generates representativesignals.

Primary reflections are those seismic waves that have reflected onlyonce, from the water bottom 112 or an interface between subterraneanformations, before being detected by a seismic receiver 110. An exampleof a primary reflection is shown in FIG. 1 by raypaths 120 and 122.Primary reflections contain the desired information about thesubterranean formations, which is the goal of marine seismic surveying.Surface multiples are those waves that have reflected multiple timesbetween the water surface 116 and any upward reflectors, such as thewater bottom 112 or formation interfaces, before being sensed by areceiver 110. An example of a surface multiple which is specifically awater bottom multiple is shown by raypaths 130, 132, 134 and 136. Anexample of an interbed multiple is shown by raypaths 140, 142, 144 and146. Interbed multiples generally have lesser downward reflectioncoefficients than surface multiples, and hence, have lower amplitudes.As previously mentioned, all such multiples are extraneous noise thatobscures the desired primary reflection signal.

FIG. 2 illustrates a flow diagram of a method 200 for computing timingerrors that occur in multiple reflections predicted by a multipleprediction algorithm in accordance with one embodiment of the invention.In one embodiment, the multiple prediction algorithm is atwo-dimensional prediction algorithm. However, the multiple predictionalgorithm may be a one-dimensional prediction algorithm, athree-dimensional prediction algorithm, or any other multiple predictionalgorithm with features that provide different levels of costeffectiveness and different kinds of seismic data sets. Method 200 maybe used to compute timing errors in various predicted multiplereflections, including predicted surface multiple reflections andpredicted interbed multiple reflections. Generally, timing errors arecreated when multiple reflections are predicted by a two-dimensionalalgorithm that ignores the inherent three-dimensional nature of seismicdata. At step 210, the actual three-dimensional travel times for theprimary reflections (actual primary travel times) and actualthree-dimensional travel times for multiple reflections (actual multipletravel times) are computed. Higher order multiple reflections may beviewed as having component segments, which may themselves be multiplereflections. For example, a second-order surface multiple reflection maybe viewed as having two components: a primary reflection and afirst-order multiple reflection. Accordingly, for the sake of notationalsimplicity, the terms “primary reflection,” “primary,” and othervariants generally refer to the component segments of a multiplereflection regardless of its order.

The complexity of this step depends on the subsurface geology and thedegree of realism desired. In one embodiment, the above-referencedcomputation in step 210 is performed by applying a ray tracing method toa three-dimensional earth model, which may take into account cross-linedip information, planar reflective and diffractive surface information.In another embodiment, the above-referenced computation is performed byapplying the ray tracing method to the three-dimensional earth model anddata acquisition geometry parameters, which may include cable-featheringinformation and source and receiver coordinates information. In yetanother embodiment, the three-dimensional earth model is a simpledipping plane water bottom model, which may be used to compute actualthree-dimensional travel times for only the water bottom primary andmultiple reflections.

Processing then continues to step 220 at which a multiple predictionalgorithm is applied to the actual three-dimensional primary traveltimes to generate travel times for multiple reflections predicted by themultiple prediction algorithm. In one embodiment, the multipleprediction algorithm used in step 220 is a two-dimensional multipleprediction algorithm. However, the multiple prediction algorithm in step220 may be a one-dimensional prediction algorithm, a three-dimensionalprediction algorithm, or any other multiple prediction algorithm withfeatures that provide different levels of cost effectiveness anddifferent kinds of seismic data sets. Such predicted multiplereflections typically contain timing errors. The multiple predictionalgorithm used in step 220 may be a surface related multiple reflectionselimination (SRME) algorithm, if the multiple reflections are surfacemultiple reflections. An example of a two-dimensional predictionalgorithm used in step 220 is described with reference to FIG. 3 below.

FIG. 3 illustrates a flow diagram of a two-dimensional predictionalgorithm 300 in accordance with one embodiment of the invention.Notably, the two-dimensional prediction algorithm 300 includes all theprocessing steps that would be necessary if the two-dimensionalprediction algorithm 300 were applied to actual seismic traces, asopposed to travel times. Steps 310, 320, 330, 340 and 360 are optional,depending on the detailed nature of the travel times being processed.Step 350, on the other hand, is a required process. Further, if step 310is not performed, then step 360 also should not be performed.

Referring now to step 310, the two-dimensional prediction algorithm 300may include the step of regularizing the actual three-dimensionalprimary travel times. This step is configured to change the actualthree-dimensional travel times, which are based on actual field datacoordinates (i.e., three-dimensional), to travel times based on nominalcoordinates (i.e., two-dimensional), which generally do not take intoaccount cross-line dip information. Step 310 is generally performedusing a differential normal moveout algorithm.

Referring now to step 320, the two-dimensional prediction algorithm 300may also include the step of extrapolating the actual three-dimensionalprimary travel times to fill in the travel times associated with missingdata traces in the field data. Generally, missing data traces are causedby having a minimum source-to-receiver offset in the field that is equalto at least several receiver intervals. Possible extrapolatingprocedures include parabolic fitting and others known to persons withordinary skill in the art.

Referring now to step 330, the two-dimensional prediction algorithm 300may further include interpolating the actual three-dimensional primarytravel times. In one embodiment, the actual three-dimensional primarytravel times may be linearly interpolated in common offset planes.Generally, step 330 is only necessary if the source and receiverintervals are unequal.

Referring now to step 340, the two-dimensional prediction algorithm 300may further include generating a set of reciprocal travel times for theactual three-dimensional primary travel times to simulate fieldacquisition with a partial or full split spread. Step 340 is configuredto allow the two-dimensional prediction algorithm 300 to avoid producingdiffraction artifacts from the zero-offset edge typically associatedwith marine field data.

Referring now to step 350, the actual three-dimensional primary traveltimes, which may have been modified by steps 310, 320, 330 and 340, areused to calculate travel times for multiple reflections predicted by atwo-dimensional prediction algorithm. In one embodiment, the traveltimes for the multiple reflections predicted by the two-dimensionalpredicted algorithm are calculated using the method(s) described in U.S.Pat. No. 6,169,959, issued to Dragoset et al., which is incorporatedherein by reference. At step 360, the travel times for the multiplereflections predicted by the two-dimensional predicted algorithm arederegularized, i.e., the travel times are mapped back from nominalcoordinates (i.e., two-dimensional) to the actual field data coordinates(i.e., three-dimensional).

Referring back to FIG. 2, at step 230, the actual three-dimensionalmultiple travel times generated at step 210 are now subtracted from thetravel times for the multiple reflections predicted by the multiplepredicted algorithm to produce travel time errors or timing errors thatoccur in the multiple reflections predicted by the multiple predictedalgorithm. The amount or size of the timing errors may be used toindicate the type of multiple prediction algorithm (i.e.,two-dimensional versus three-dimensional) to be used to remove themultiple reflections.

Typically, multiple reflections predicted by a two-dimensional multipleprediction algorithm are adaptively subtracted from a recorded seismicdata set to produce a multiple-free seismic data set. As such, if theaforementioned timing errors are larger than the desirable matchingfilter lengths (which are typically in units of time), then the adaptivesubtraction algorithm using the two-dimensional prediction algorithmwill not be able to compensate for the timing errors, and thereby fail.In this manner, method 200 may be configured to indicate as to when itwould be necessary to perform a three-dimensional prediction algorithmversus a two-dimensional prediction algorithm to remove the multiplereflections from the recorded seismic data set.

In accordance with one embodiment of the invention, computation of thetiming errors may also be used to optimize a marine survey design. FIG.4 illustrates a method 400 for optimizing a marine survey design inaccordance with one embodiment of the invention. At step 410, a crossline aperture is calculated. The cross line aperture is generallydefined by two-dimensional coordinates of surface reflection points thatare associated with the multiple reflections. In one embodiment, thecross line aperture is calculated by applying a ray tracing method to athree-dimensional earth model, as described in the previous paragraphs.In another embodiment, the cross line aperture is calculated by applyinga ray tracing method to a three-dimensional earth model and dataacquisition geometry, such as cable feathering information. At step 420,a determination is made as to whether a previous cross line apertureexists. If the answer is in the negative, then processing returns tostep 410. If the answer is in the affirmative, then a comparison is madebetween the current cross line aperture and the previous cross lineaperture (step 430). At step 440, the data acquisition geometry ismodified to optimize the cross line aperture according to the result ofthe comparison. In one embodiment, the data acquisition geometry ismodified to minimize the cross line aperture. In another embodiment,modifying the data acquisition geometry includes modifying thecable-feathering using steerable streamers. In yet another embodiment,modifying the data acquisition geometry includes modifying the receivercross line spread width to encompass the cross line aperture. Theoptimization method 400 may be performed during a line change.

In accordance with another embodiment, the timing errors computed usingthe methods described above may be used to improve an adaptivesubtraction algorithm. For example, the timing errors may be used toconstrain or adjust the matching filters used in the adaptivesubtraction algorithm. In another example, the timing errors may be usedto adjust multiples produced by a multiple prediction algorithm, such asa two-dimensional multiple prediction algorithm, in which the adjustmentincludes applying a static time shift to each trace. In such an example,the multiples produced by the multiple prediction algorithm are splitinto segments, and each timing error-based adjustment is applied to eachsegment. In one embodiment, a suite of adjustments based on the timingerrors may be applied to the multiples produced by multiple predictionalgorithm to create a suite of possible multiple models, and amultidimensional adaptive subtraction algorithm is then used tooptimally filter and subtract the suite of possible multiple models froma recorded seismic data set.

FIG. 5 illustrates a computer network 500, into which embodiments of theinvention may be implemented. The computer network 500 includes a systemcomputer 530, which may be implemented as any conventional personalcomputer or workstation, such as a UNIX-based workstation. The systemcomputer 530 is in communication with disk storage devices 529, 531, and533, which may be external hard disk storage devices. It is contemplatedthat disk storage devices 529, 531, and 533 are conventional hard diskdrives, and as such, will be implemented by way of a local area networkor by remote access. Of course, while disk storage devices 529, 531, and533 are illustrated as separate devices, a single disk storage devicemay be used to store any and all of the program instructions,measurement data, and results as desired.

In one embodiment, seismic data from geophones and positionalinformation are stored in disk storage device 531. The system computer530 may retrieve the appropriate data from the disk storage device 531to perform the timing errors computation according to programinstructions that correspond to the methods described herein. Theprogram instructions may be written in a computer programming language,such as C++, Java and the like. The program instructions may be storedin a computer-readable memory, such as program disk storage device 533.Of course, the memory medium storing the program instructions may be ofany conventional type used for the storage of computer programs,including hard disk drives, floppy disks, CD-ROMs and other opticalmedia, magnetic tape, and the like.

According to the preferred embodiment of the invention, the systemcomputer 530 presents output primarily onto graphics display 527, oralternatively via printer 528. The system computer 530 may store theresults of the methods described above on disk storage 529, for lateruse and further analysis. The keyboard 526 and the pointing device(e.g., a mouse, trackball, or the like) 525 may be provided with thesystem computer 530 to enable interactive operation.

The system computer 530 may be located at a data center remote from thesurvey region. The system computer 530 is in communication withgeophones (either directly or via a recording unit, not shown), toreceive signals indicative of the reflected seismic energy. Thesesignals, after conventional formatting and other initial processing, arestored by the system computer 530 as digital data in the disk storage531 for subsequent retrieval and processing in the manner describedabove. While FIG. 5 illustrates the disk storage 531 as directlyconnected to the system computer 530, it is also contemplated that thedisk storage device 531 may be accessible through a local area networkor by remote access. Furthermore, while disk storage devices 529, 531are illustrated as separate devices for storing input seismic data andanalysis results, the disk storage devices 529, 531 may be implementedwithin a single disk drive (either together with or separately fromprogram disk storage device 533), or in any other conventional manner aswill be fully understood by one of skill in the art having reference tothis specification.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. A method for computing one or more timing errors that occur in one ormore multiple reflections predicted by a multiple prediction algorithm,comprising: (a) generating one or more actual three-dimensional primarytravel times and one or more actual three-dimensional multiple traveltimes from an actual seismic survey having cable feathering information;(b) applying a first multiple prediction algorithm to the actualthree-dimensional primary travel times to generate one or more traveltimes for the multiple reflections predicted by the first multipleprediction algorithm; (c) subtracting the actual three-dimensionalmultiple travel times from the travel times for the multiple reflectionspredicted by the first multiple prediction algorithm, thereby generatingone or more timing errors that occur in the multiple reflectionspredicted by the first multiple prediction algorithm; (d) determiningwhether the first multiple prediction algorithm is sufficient to removethe one or more multiple reflections from the seismic data based on oneor more sizes of the timing errors; and (e) wherein step (b), (c) or (d)is performed by program instructions accessed by a computer system. 2.The method of claim 1, wherein the multiple prediction algorithm is oneof a one-dimensional multiple prediction algorithm, a two-dimensionalmultiple prediction algorithm and a three-dimensional multipleprediction algorithm.
 3. The method of claim 1, wherein generating theactual three-dimensional travel times comprises applying a ray tracingmethod to a three-dimensional earth model.
 4. The method of claim 3,wherein the three-dimensional earth model includes cross-line dipinformation.
 5. The method of claim 3, wherein the three-dimensionalearth model includes planar reflective information.
 6. The method ofclaim 3, wherein the three-dimensional earth model includes diffractivesurface information.
 7. The method of claim 3, wherein generating theactual three-dimensional travel times comprises applying a ray tracingmethod to the three-dimensional earth model and one or more dataacquisition geometry parameters.
 8. The method of claim 7, wherein thedata acquisition geometry includes the cable-feathering information. 9.The method of claim 1, wherein the multiple reflections are one ofsurface multiple reflections and interbed multiple reflections.
 10. Themethod of claim 1, wherein applying the multiple prediction algorithm tothe actual three-dimensional primary travel times comprises regularizingthe actual three-dimensional primary travel times.
 11. The method ofclaim 1, wherein applying the multiple prediction algorithm to theactual three-dimensional primary travel times comprises extrapolatingthe actual three-dimensional primary travel times to fill in one or moretravel times associated with one or more missing data traces.
 12. Themethod of claim 1, wherein applying the multiple prediction algorithm tothe actual three-dimensional primary travel times comprisesinterpolating the actual three-dimensional primary travel times.
 13. Themethod of claim 1, wherein applying the multiple prediction algorithm tothe actual three-dimensional primary travel times comprises generating aset of reciprocal travel times for the actual three-dimensional primarytravel times.
 14. The method of claim 1, further comprising optimizing amarine survey design based on the timing errors.
 15. The method of claim14, wherein the marine survey design is optimized by: modifying one ormore data acquisition geometry control parameters to minimize a crossline aperture.
 16. The method of claim 15, wherein the data acquisitiongeometry control parameters comprise streamer cable feathering controlparameters.
 17. The method of claim 14, wherein the marine survey designis optimized by modifying a receiver cross line spread width toencompass the cross line aperture.
 18. The method of claim 14, whereinthe marine survey design is optimized during a line change.
 19. Themethod of claim 1, further comprising adjusting one or more matchingfilters in an adaptive subtraction algorithm based on the timing errors,wherein the adaptive subtraction algorithm is configured to subtract oneor more predicted multiple reflections from a plurality of seismic datatraces.
 20. The method of claim 19, wherein adjusting the multiplereflections comprises applying a static time shift to a plurality ofseismic data traces.
 21. The method of claim 1, further comprisingadjusting one or more multiple reflections predicted by the multipleprediction algorithm based on the timing errors.
 22. The method of claim19, wherein adjusting the multiple reflections comprises: splitting themultiple reflections produced by the multiple prediction algorithm intoa plurality of segments; and adjusting the plurality of segments basedon the timing errors.
 23. The method of claim 1, further comprising:adjusting the multiple reflections predicted by the multiple predictionalgorithm based on the timing errors, thereby creating a plurality ofpossible multiple reflection models; and optimally filtering andsubtracting the possible multiple reflection models from a plurality ofseismic data traces based on a multidimensional adaptive subtractionalgorithm.
 24. The method of claim 1, further comprising: repeatingsteps (b)-(d) for a second multiple prediction algorithm; and comparingthe timing errors that occur in the multiple reflections predicted bythe first multiple prediction algorithm with the timing errors thatoccur in the multiple reflections predicted by the second multipleprediction algorithm to determine which of the first multiple predictionalgorithm or the second multiple prediction algorithm is more accurate.25. The method of claim 1, wherein the multiple prediction algorithmcomprises any preprocessing step that prepares recorded traces formultiple prediction.
 26. An apparatus for computing one or more timingerrors that occur in one or more multiple reflections predicted by amultiple prediction algorithm, comprising: means for generating one ormore actual three-dimensional primary travel times and one or moreactual three-dimensional multiple travel times from an actual seismicsurvey having cable feathering information; means for applying a firstmultiple prediction algorithm to the actual three-dimensional primarytravel times to generate one or more travel times for the multiplereflections predicted by the first multiple prediction algorithm; meansfor subtracting the actual three-dimensional multiple travel times fromthe travel times for the multiple reflections predicted by the firstmultiple prediction algorithm, thereby generating the timing errors thatoccur in the multiple reflections predicted by the first multiplepredicted algorithm; and means for using the timing errors to evaluatethe performance of the first multiple prediction algorithm.
 27. Theapparatus of claim 26, wherein the means for generating the actualthree-dimensional primary travel times and the actual three-dimensionalmultiple travel times comprises means for applying a ray tracing methodto a three-dimensional earth model and one or more data acquisitiongeometry having the cable-feathering information and source and receivercoordinates.
 28. A method for computing one or more timing errors thatoccur in one or more multiple reflections predicted by a multipleprediction algorithm, comprising: generating one or more actualthree-dimensional primary travel times and one or more actualthree-dimensional multiple travel times from an actual seismic surveyhaving cable feathering information; applying a first multiple surfacerelated multiple reflections elimination (SRME) algorithm to the actualthree-dimensional primary travel times to generate one or more traveltimes for the multiple reflections predicted by the first multipleprediction (SRME) algorithm; subtracting the actual three-dimensionalmultiple travel times from the travel times for the multiple reflectionspredicted by the first multiple prediction SRME algorithm, therebygenerating the timing errors that occur in the multiple reflectionspredicted by the first multiple prediction SRME algorithm; determiningwhether the first multiple prediction algorithm is sufficient to removethe one or more multiple reflections from the seismic data based on oneor more sizes of the timing errors; and wherein the application step,the subtraction step or the determining step is performed by programinstructions accessed by a computer system.
 29. The method of claim 28,wherein the multiple reflections are surface multiple reflections. 30.The method of claim 28, wherein the multiple SRME algorithm is a twodimensional (2D) SRME algorithm.